• 제목/요약/키워드: Marquardt method.

검색결과 101건 처리시간 0.027초

실측 데이터를 이용한 일반용부하와 가정용부하의 부하모델 추정방안 (Load model estimation method for residential load and nomal load using measured data)

  • 박래준;권오성;송경빈;김규호;박정욱;조종만;이성무
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2011년도 제42회 하계학술대회
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    • pp.606-607
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    • 2011
  • 전력계통의 부하를 모델링하기 위해서는 부하 모델 구조의 선정과 부하모델 구조의 파라미터를 추정하는 방법이 필요하다. 부하 모델의 구조는 ZIP모델을 사용하고, 부하 모델의 파라미터를 추정하는 방법으로는 Levenberg-Marquardt방법을 사용하여 한국전력공사 변전소이차 측에서 측정된 실측 데이터를 이용하여 부하를 모델링하였다. 또한 모델링된 부하의 대표파라미터를 선정하고 대표파라미터를 실제 계통에 적용하였을 때의 오차를 분석하였다.

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Sliding mode control based on neural network for the vibration reduction of flexible structures

  • Huang, Yong-An;Deng, Zi-Chen;Li, Wen-Cheng
    • Structural Engineering and Mechanics
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    • 제26권4호
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    • pp.377-392
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    • 2007
  • A discrete sliding mode control (SMC) method based on hybrid model of neural network and nominal model is proposed to reduce the vibration of flexible structures, which is a robust active controller developed by using a sliding manifold approach. Since the thick boundary layer will reduce the virtue of SMC, the multilayer feed-forward neural network is adopted to model the uncertainty part. The neural network is trained by Levenberg-Marquardt backpropagation. The design objective of the sliding mode surface is based on the quadratic optimal cost function. In course of running, the input signal of SMC come from the hybrid model of the nominal model and the neural network. The simulation shows that the proposed control scheme is very effective for large uncertainty systems.

인공신경망을 이용한 뿌리산업 생산공정 예측 모델 개발 (Development of Prediction Model for Root Industry Production Process Using Artificial Neural Network)

  • 박찬범;손흥선
    • 한국정밀공학회지
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    • 제34권1호
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    • pp.23-27
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    • 2017
  • This paper aims to develop a prediction model for the product quality of a casting process. Prediction of the product quality utilizes an artificial neural network (ANN) in order to renovate the manufacturing technology of the root industry. Various aspects of the research on the prediction algorithm for the casting process using an ANN have been investigated. First, the key process parameters have been selected by means of a statistics analysis of the process data. Then, the optimal number of the layers and neurons in the ANN structure is established. Next, feed-forward back propagation and the Levenberg-Marquardt algorithm are selected to be used for training. Simulation of the predicted product quality shows that the prediction is accurate. Finally, the proposed method shows that use of the ANN can be an effective tool for predicting the results of the casting process.

Joint Modeling of Death Times and Counts Using a Random Effects Model

  • Park, Hee-Chang;Klein, John P.
    • Journal of the Korean Data and Information Science Society
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    • 제16권4호
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    • pp.1017-1026
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    • 2005
  • We consider the problem of modeling count data where the observation period is determined by the survival time of the individual under study. We assume random effects or frailty model to allow for a possible association between the death times and the counts. We assume that, given a random effect, the death times follow a Weibull distribution with a rate that depends on some covariates. For the counts, given the random effect, a Poisson process is assumed with the intensity depending on time and the covariates. A gamma model is assumed for the random effect. Maximum likelihood estimators of the model parameters are obtained. The model is applied to data set of patients with breast cancer who received a bone marrow transplant. A model for the time to death and the number of supportive transfusions a patient received is constructed and consequences of the model are examined.

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손실 반공간에 묻힌 2차원 원통형 파이프의 검출 및 식별 (Iterative Reconstruction of Multiple Cylinders Buried in the Lossy Half Space)

  • Kim, Jeong-Seok;Ra, Jung-Woong
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2001년도 하계종합학술대회 논문집(1)
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    • pp.173-176
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    • 2001
  • Several dielectric as well as conducting cylinders buried in the lossy half space are reconstructed from the scattered fields measured along the interface between the air and the lossy ground. Iterative inversion method by using the hybrid optimization algorithm combining the genetic and the Levenberg-Marquardt algorithm enables us to find the positions, the sizes, and the medium parameters such as the permittivities and the conductivities of the buried cylinders as well as those of the background lossy half space. Illposedness of the inversion caused by the errors in the measured scattered fields are regularized by filtering the evanescent modes of the scattered fields out.

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신경회로망 예측 알고리즘을 적용한 TCP-Friednly 제어 방법 (A TCP-Friendly Control Method using Neural Network Prediction Algorithm)

  • 유성구;정길도
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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    • pp.105-107
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    • 2006
  • As internet streaming data increase, transport protocol such as TCP, TGP-Friendly is important to study control transmission rate and share of Internet bandwidth. In this paper, we propose a TCP-Friendly protocol using Neural Network for media delivery over wired Internet which has various traffic size(PTFRC). PTFRC can effectively send streaming data when occur congestion and predict one-step ahead round trip time and packet loss rate. A multi-layer perceptron structure is used as the prediction model, and the Levenberg-Marquardt algorithm is used as a traning algorithm. The performance of the PTFRC was evaluated by the share of Bandwidth and packet loss rate with various protocols.

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PDA를 이용한 휴대용 Electronic Nose 시스템 개발 (Design of a Potable Electronic Nose System using PDA)

  • 김정도;변형기;함유경
    • 센서학회지
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    • 제13권6호
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    • pp.454-461
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    • 2004
  • We have designed a portable electronic nose (e-nose) system using an array of commercial gas sensors and personal digital assistants (PDA) for the recognition and analysis of volatile organic compounds (VOC) in the field. Field screening of pollutants has been a target of instrumental development during the past years. A portable e-nose system was advantageous to localize the special extent of a pollution or to find pollutants source. The employment of PDA improved the user-interface and data transfer by Internet from on-site to remote computer. We adapted the Lavenberg-Marquardt algorithm based on the back-propagation and proposed the method that could predict the concentration levels of VOC gases after classification by separating neural network into two parts.

인공 신경망을 이용한 플랫 슬래브 주차장 구조물의 등가차량하증계수 (Determination of Equivalent Vehicle Load Factors for Flat Slab Parking Structures Using Artificial Neural Networks)

  • 곽효경;송종영;이기장;이정원
    • 한국전산구조공학회:학술대회논문집
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    • 한국전산구조공학회 2002년도 봄 학술발표회 논문집
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    • pp.233-240
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    • 2002
  • In this paper, the effects of vehicle loads on flat slab system are investigated on the basis of the previous studies for beam-girder parking structural system. The influence surfaces of flat slab for typical design section are developed for the purpose of obtaining maximum member forces under vehicle loads. In addition, the equivalent vehicle load factors for flat slab parking structures are suggested using artificial neural network. The network responses are compared with the results by numerical analyses to verify the validation of Levenberg-Marquardt algorithm adopted as training method in this paper. Many parameter studies fur the flat slab structural system show dominant vehicle load effects at the center positive moments in both column and middle strips, like the beam-girder parking structural system.

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Nonlinear viscous material model

  • Ivica Kozar;Ivana Ban;Ivan Zambon
    • Coupled systems mechanics
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    • 제12권5호
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    • pp.419-428
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    • 2023
  • We have developed a model for estimating the parameters of viscous materials from indirect tensile tests for asphalt. This is a simple Burger nonlinear rheological two-cell model or standard model. At the same time, we begin to develop a more versatile and complex multi-cell model. The simple model is validated using experimental load-displacement results from laboratory tests: The recorded displacements are used as input values and the measured force data are simulated with the model. The optimal model parameters are estimated using the Levenberg-Marquardt method and a very good agreement between the experimental results and the model calculations is shown. However, not all parts of the model are active in the loading phase of the experiment, so we extended the validation of the model to the simulation of the relaxation behaviour. In this stage, the other model parameters are activated and the simulation results are consistent with the literature. At this stage, we have estimated the parameters only for the two-cell uniaxial model, but further work will include results for the multi-cell model.

Extraction of Passive Device Model Parameters Using Genetic Algorithms

  • Yun, Il-Gu;Carastro, Lawrence A.;Poddar, Ravi;Brooke, Martin A.;May, Gary S.;Hyun, Kyung-Sook;Pyun, Kwang-Eui
    • ETRI Journal
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    • 제22권1호
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    • pp.38-46
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    • 2000
  • The extraction of model parameters for embedded passive components is crucial for designing and characterizing the performance of multichip module (MCM) substrates. In this paper, a method for optimizing the extraction of these parameters using genetic algorithms is presented. The results of this method are compared with optimization using the Levenberg-Marquardt (LM) algorithm used in the HSPICE circuit modeling tool. A set of integrated resistor structures are fabricated, and their scattering parameters are measured for a range of frequencies from 45 MHz to 5 GHz. Optimal equivalent circuit models for these structures are derived from the s-parameter measurements using each algorithm. Predicted s-parameters for the optimized equivalent circuit are then obtained from HSPICE. The difference between the measured and predicted s-parameters in the frequency range of interest is used as a measure of the accuracy of the two optimization algorithms. It is determined that the LM method is extremely dependent upon the initial starting point of the parameter search and is thus prone to become trapped in local minima. This drawback is alleviated and the accuracy of the parameter values obtained is improved using genetic algorithms.

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